Types of Location Data

Location data is being utilized by thousands of companies to gain insights into who, how, and why people move from place to place. Travel patterns act as an informational resource for improving efficiency across various industries. For example, retail companies use location data to identify their target demographic and create optimal advertising strategies. Location data can also be used for commercial real estate when considering where to build a new business. And travel patterns are the core of city planning efforts to build enjoyable and sustainable communities. But some may be wondering, where does this data come from?


Location data on the movement of people are gathered using four different technologies: indoor positioning systems, mobile phone applications, “always on” mobile phone applications, and cellular locations. Data gathered from each of these sources are aggregated and anonymized to protect the public’s privacy. From the available data, patterns are scaled to the size of the population, providing information on where people are, where they travel, which mode of transportation they use, along which routes, and more.


The chart below details the four major sources of consumer level location data. Each source varies in terms of precision (ranging from 1 meter to 100 meters) and persistence (pings per user). Higher precision confirms the true location of the user. Persistence describes the stream of location data points from a single user that can be categorized to infer movement patterns.



An indoor positioning system is like a local Global Positioning System (GPS), which can be used to understand how people move within an enclosed environment. These systems can use a variety of data from motion sensors, door counters, and consumer phone connections such as WiFi or Bluetooth, to identify movement patterns throughout an indoor space. Indoor positioning systems are largely used by retail companies to capture the movement of each individual who walks through their doors. Due to the small-scale nature inherent with this technology, precision is very high but loses a person’s movement when they leave the enclosed area.


Mobile phone applications can share a user’s location while the app is in use anywhere in the world. App-based location data can be shared with digital advertising markets, allowing companies to better target potential customers while they are in specific locations by setting a unique boundary, so that when a potential customer enters the targeted location, it allows you to send them targeted messages. This is called Geofencing and is beneficial to companies wanting to know a prospective client or user’s location at a specific unique time. Apps still provide relatively high precision for larger scale analysis. However, most downloaded apps are rarely used which makes it difficult for an app to share multiple locations from a single user.


A select few mobile phone applications constantly share their location and can provide multiple precise locations for each user every day. These “always on” apps are created for a variety of purposes including lifestyle, dating, travel, safety, e-commerce, navigation, and weather. Always on apps have both precision and persistence, but limited sample size compared with the other technologies. Multiple pings per user per day shows trends of the user’s movement. When this data is anonymized and aggregated with other’s movement, it is possible to understand population-wide movement patterns.


Similarly, cellular location data is also useful for understanding population movement. User location data comes from mobile carriers that are constantly triangulating devices between cell towers. Although cellular location data cannot identify how often a store is visited, it can understand how whole neighborhood populations move. For what cellular location lacks in precision, it makes up in persistent sample size.


Unfortunately, only studying a single technology sample leaves the sample prone to bias. Sampling bias occurs when a sample is collected in such a way that some members of the intended population are less likely to be included than others. For example, it might seem like everyone has a mobile phone, but not everyone does. And for those who do, those phones are not powered on or in use at all times. It is important to understand that only using location data from a single technology will create a sample of movement and it will leave out a significant portion of the total population.


For the most accurate understanding of total population movement, different data sources can be combined to paint a complete picture. For example, retailers combine their transactional data with their indoor positioning data to understand approximately how many people enter each store, as well as their home locations and their demographics.


Identifying the right type of location data is largely dependent on how the data will be used. If the intended use is to understand movements within a specific store, indoor positioning systems are ideal. If the data will be used to improve advertising strategies to people who are in a specific store, general app location data is best. If the intended use is understanding who is passing by a store, changes in mobility, urban development, out-of-home (OOH) advertising, auto risk, or anything else with regards to the total population, then Citilabs’ Streetlytics is the best.


How Citilabs Uses Location Data to Create a More Efficient Urban Landscape

Citilabs has built the world’s leading transportation modeling and simulation software, Cube, used in 2,500 cities around the world. Citilabs now combines its simulation software with a variety of location data and measured movement to create Streetlytics, a monthly measurement of total population movement in the United States. Streetlytics provides hourly volumes, speeds, home neighborhoods, and demographics for all vehicles and pedestrians. This includes the origins and destinations for every trip, as well as the turn-by-turn path throughout the route.

Figure 1: Vehicle volume statistics by day of the week along McCully Street in Honolulu, Hawaii using Streetlytics software


Streetlytics applies varying weights to the millions of Referenced, Sampled, and Modeled movements based on their characteristics and quality of the underlying data. The independent views are then combined in to an optimized understanding of total population movement nationwide. This process produces a robust and accurate understanding of the total moving population. Telling us where people are coming from and going to, what they pass, when they travel, where they live and work, and what mode of travel they are using.

Figure 2: The millions of Referenced, Sampled, and Modeled movements are combined in to an optimized understanding of total population movement nationwide.


Since 2017, Streetlytics has become the data that powers the audience location measurement for Geopath, the nonprofit auditing organization for the $8 billion OOH media industry in the United States. The American billboard industry utilizes travel metrics for advertising strategies. This includes ad exposure time, demographic viewership, and peak timing.


Streetlytics’ comprehensive understanding of population movement has been applied to a variety of industries including site selection for commercial real estate, driver risk analysis for auto insurance, and transportation infrastructure demand for mobility companies and government agencies.


Please contact us if you would like to find out more about Streetlytics and how it can be used.


Contact the author:
Hugh Malkin
(+1) 404 993 4168